Mixed convexity & optimization of the SVM QP problem for nonlinear polynomial kernel maps

Authors:
Emre Tokgöz;Theodore B. Trafalis
Affiliations:
School of Industrial Engineering, University of Oklahoma, Norman, OK;Department of Mathematics, University of Oklahoma, Norman, OK
Venue:
Proceedings of the 15th WSEAS international conference on Computers
Year:
2011

Citing 3
Cited 0

The nature of statistical learning theory

The nature of statistical learning theory
Transductive Inference for Text Classification using Support Vector Machines

ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
Sharp and simple bounds for the Erlang delay and loss formulae

Queueing Systems: Theory and Applications

Quantified Score

Hi-index	0.00

Visualization

Abstract

Support vector machine (SVM) can be used for regression analysis and data classification of a data set. In particular, non-linear classification of a given data set is possible by using the (inhomogeneous) polynomial kernel k (x, y) = (1 + xT ċ y)d. In this work, the SVM QP problem with (inhomogeneous) polynomial kernel k (x, y) is expressed as a mixed convex optimization problem with respect to the real variables α ∈ Rl and b ∈ R; and the integer variable d ∈ Z.